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Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

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Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning

T Meeradevi¹, S Sasikala¹, L Murali²

¹Department of ECE, Kongu Engineering College, Erode, Tamil Nadu, India.

Scientific Reports

|March 13, 2025

Summary

This summary is machine-generated.

This study enhances lung disease detection using machine learning (ML) and deep learning (DL) on X-ray images. The deep learning model achieved 97.05% accuracy, outperforming ML methods for classifying benign or malignant lung conditions.

Keywords:

Deep learning Lung disease Machine learning TOPSIS

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Respiratory Medicine

Background:

Chronic respiratory diseases stem from airway and lung conditions, often caused by tobacco smoke, environmental pollutants, and childhood infections.
Early detection of lung diseases through medical image analysis is crucial for effective patient treatment.
Classifying lung X-ray images and identifying specific diseases like Atelectasis, Infiltration, Nodule, and Pneumonia aids diagnosis.

Purpose of the Study:

To classify lung X-ray images as benign or malignant.
To identify specific malignant lung diseases including Atelectasis, Infiltration, Nodule, and Pneumonia.
To compare the efficacy of machine learning (ML) and deep learning (DL) models for lung disease classification.

Main Methods:

Utilized machine learning (ML) approaches combined with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank classifiers.
Proposed the deep learning (DL) model Inception v3 for lung X-ray image analysis.
Evaluated and ranked Support Vector Machine (SVM) with Radial Basis Function (RBF) as a top-performing ML classifier.

Main Results:

The Support Vector Machine (SVM) with Radial Basis Function (RBF) was identified as the best classifier among the ML methods evaluated.
The deep learning (DL) model, Inception v3, achieved a superior accuracy of 97.05%.
The DL approach demonstrated an 11.8% improvement in accuracy compared to the ML methods on the same dataset.

Conclusions:

Deep learning models, specifically Inception v3, offer significantly higher accuracy for lung disease classification from X-ray images compared to traditional ML approaches.
The integration of ML and DL techniques provides a robust framework for the early and accurate detection of various lung conditions.
Accurate classification of lung X-rays aids in timely diagnosis and treatment planning for patients with chronic respiratory diseases.